Undermind Research Topic: Machine learning applications in personalized nutrition for chronic disease management. I want articles that use machine learning to tailor nutritional advice for managing chronic diseases.

Research topic

Machine learning applications in personalized nutrition for chronic disease management. I want articles that use machine learning to tailor nutritional advice for managing chronic diseases.

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References

Last 5 years

Last 2 years

> 1 citation per year

> 5 citations per year

Topic Match

Cit./Year

Year

Paper

Paper Relevance Summary

99.8%

2.9

2021

[1] Biomarker-based deep learning for personalized nutrition Dimitrios P. Panagoulias, ..., and G. Tsihrintzis 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) 2021 - 9 citations - Show abstract - Cite 99.8% topic match

Develops deep learning-based personalized nutrition software. Utilizes biomarkers to tailor dietary recommendations, enhancing patient management. Demonstrates efficacy through evaluation on real data, indicating high performance.

99.6%

18.5

2020

[2] Realizing an Efficient IoMT-Assisted Patient Diet Recommendation System Through Machine Learning Model C. Iwendi, ..., and F. Noor IEEE Access 2020 - 91 citations - Show abstract - Cite Abstract: Recent studies have shown that robust diets recommended to patients by Dietician or an Artificial Intelligent automated medical diet based cloud system can increase longevity, protect against further disease, and improve the overall quality of life. However, medical personnel are yet to fully understand patient-dietician’s rationale of recommender system. This paper proposes a deep learning solution for health base medical dataset that automatically detects which food should be given to which patient base on the disease and other features like age, gender, weight, calories, protein, fat, sodium, fiber, cholesterol. This research framework is focused on implementing both machine and deep learning algorithms like, logistic regression, naive bayes, Recurrent Neural Network (RNN), Multilayer Perceptron (MLP), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM). The medical dataset collected through the internet and hospitals consists of 30 patient’s data with 13 features of different diseases and 1000 products. Product section has 8 features set. The features of these IoMT data were analyzed and further encoded before applying deep and machine and learning-based protocols. The performance of various machine learning and deep learning techniques was carried and the result proves that LSTM technique performs better than other scheme with respect to forecasting accuracy, recall, precision, and <inline-formula> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula>-measures. We achieved 97.74% accuracy using LSTM deep learning model. Similarly 98% precision, 99% recall and <inline-formula> <tex-math notation="LaTeX">$99\%~F1$ </tex-math></inline-formula>-measure for allowed class is achieved, and for not-allowed class precision is 89%, recall score is 73% and <inline-formula> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> Measure score is 80%. 99.6% topic match

Introduces a machine learning-assisted diet recommendation system. Utilizes logistic regression, naive bayes, RNN, MLP, GRU, and LSTM for tailoring patient diets. Focuses on disease-based food recommendations influenced by patient-specific data like age and dietary needs.

98.4%

0.8

2023

[3] Predicting Cardiovascular Disease Mortality: Leveraging Machine Learning for Comprehensive Assessment of Health and Nutrition Variables Agustin Martin-Morales, ..., and Michihiro Araki Nutrients 2023 - 1 citations - Show abstract - Cite 98.4% topic match

Provides ML-based analysis of dietary and health data for CVD. Random forests algorithm used to predict CVD mortality from health/nutrition variables. Emphasizes dietary intake's importance, suggesting potential for personalized nutrition advice.

97.3%

0.0

2022

[4] RDED: Recommendation of Diet and Exercise for Diabetes Patients using Restricted Boltzmann Machine Muhammad Sajid, ..., and Muhammad Fuzail VFAST Transactions on Software Engineering 2022 - 0 citations - Show abstract - Cite 97.3% topic match

Utilizes Restricted Boltzmann Machines for dietary and exercise recommendations. Targets diabetes patients by tailoring nutrition and activity based on medical data and preferences. Focuses on personalization through user-food ratings, impacting chronic disease management.

95.5%

0.0

2023

[5] Visual Food Intake Monitoring System for Diabetes Management Ambika Jadoonanan and Sean Rocke 2023 5th International Conference on Bio-engineering for Smart Technologies (BioSMART) 2023 - 0 citations - Show abstract - Cite 95.5% topic match

Provides a deep learning approach to diabetes management. Utilizes neural networks for food segmentation and nutritional estimation in diabetes dietary management. Investigates incremental transfer learning for personalized diet adaptation.

95.4%

3.6

2023

[6] Interpretable Machine Learning for Personalized Medical Recommendations: A LIME-Based Approach Yuanyuan Wu, ..., and Mengxing Huang Diagnostics 2023 - 5 citations - Show abstract - Cite Abstract: Chronic diseases are increasingly major threats to older persons, seriously affecting their physical health and well-being. Hospitals have accumulated a wealth of health-related data, including patients’ test reports, treatment histories, and diagnostic records, to better understand patients’ health, safety, and disease progression. Extracting relevant information from this data enables physicians to provide personalized patient-treatment recommendations. While collaborative filtering techniques and classical algorithms such as naive Bayes, logistic regression, and decision trees have had notable success in health-recommendation systems, most current systems primarily inform users of their likely preferences without providing explanations. This paper proposes an approach of deep learning with a local interpretable model–agnostic explanations (LIME)-based interpretable recommendation system to solve this problem. Specifically, we apply the proposed approach to two chronic diseases common in older adults: heart disease and diabetes. After data preprocessing, we use six deep-learning algorithms to form interpretations. In the heart-disease data set, the actual model recommendation of multi-layer perceptron and gradient-boosting algorithm differs from the local model’s recommendation of LIME, which can be used as its approximate prediction. From the feature importance of these two algorithms, it can be seen that the CholCheck, GenHith, and HighBP features are the most important for predicting heart disease. In the diabetes data set, the actual model predictions of the multi-layer perceptron and logistic-regression algorithm were little different from the local model’s prediction of LIME, which can be used as its approximate recommendation. Moreover, from the feature importance of the two algorithms, it can be seen that the three features of glucose, BMI, and age were the most important for predicting heart disease. Next, LIME is used to determine the importance of each feature that affected the results of the calculated model. Subsequently, we present the contribution coefficients of these features to the final recommendation. By analyzing the impact of different patient characteristics on the recommendations, our proposed system elucidates the underlying reasons behind these recommendations and enhances patient trust. This approach has important implications for medical recommendation systems and encourages informed decision-making in healthcare. 95.4% topic match

Introduces a LIME-based machine learning system for medical advice. Targets personalized recommendations for managing heart disease and diabetes in older adults. Utilizes deep learning algorithms for interpretable health recommendations.

95.2%

0.7

2022

[7] Improving Outcomes Through Personalized Recommendations in a Remote Diabetes Monitoring Program: Observational Study Sowmya S Kamath, ..., and Anmol Madan JMIR Formative Research 2022 - 2 citations - Show abstract - Cite Abstract: Background Diabetes management is complex, and program personalization has been identified to enhance engagement and clinical outcomes in diabetes management programs. However, 50% of individuals living with diabetes are unable to achieve glycemic control, presenting a gap in the delivery of self-management education and behavior change. Machine learning and recommender systems, which have been used within the health care setting, could be a feasible application for diabetes management programs to provide a personalized user experience and improve user engagement and outcomes. Objective This study aims to evaluate machine learning models using member-level engagements to predict improvement in estimated A1c and develop personalized action recommendations within a remote diabetes monitoring program to improve clinical outcomes. Methods A retrospective study of Livongo for Diabetes member engagement data was analyzed within five action categories (interacting with a coach, reading education content, self-monitoring blood glucose level, tracking physical activity, and monitoring nutrition) to build a member-level model to predict if a specific type and level of engagement could lead to improved estimated A1c for members with type 2 diabetes. Engagement and improvement in estimated A1c can be correlated; therefore, the doubly robust learning method was used to model the heterogeneous treatment effect of action engagement on improvements in estimated A1c. Results The treatment effect was successfully computed within the five action categories on estimated A1c reduction for each member. Results show interaction with coaches and self-monitoring blood glucose levels were the actions that resulted in the highest average decrease in estimated A1c (1.7% and 1.4%, respectively) and were the most recommended actions for 54% of the population. However, these were found to not be the optimal interventions for all members; 46% of members were predicted to have better outcomes with one of the other three interventions. Members who engaged with their recommended actions had on average a 0.8% larger reduction in estimated A1c than those who did not engage in recommended actions within the first 3 months of the program. Conclusions Personalized action recommendations using heterogeneous treatment effects to compute the impact of member actions can reduce estimated A1c and be a valuable tool for diabetes management programs in encouraging members toward actions to improve clinical outcomes. 95.2% topic match

Evaluates machine learning for personalized diabetes management. Uses machine learning to predict A1c improvement and recommend actions in remote monitoring. Focuses on diabetes, a chronic disease, ensuring high relevance to tailored nutritional advice.

88.5%

3.2

2021

[8] Nutritional biomarkers and machine learning for personalized nutrition applications and health optimization Dimitrios P. Panagoulias, ..., and G. Tsihrintzis 2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) 2021 - 11 citations - Show abstract - Cite 88.5% topic match

Employs machine learning to analyze nutritional biomarkers. Uses neural networks for general evaluation of biomarkers and predicting dietary patterns. Focuses on metabolomics to provide insights into personalized nutrition but not directly on chronic disease management.

87.3%

0.0

2023

[9] Precision cardiodiet: transforming cardiac care with artificial intelligence-driven dietary recommendations Shahadat Hoshen Moz, ..., and Syed Md. Galib Radioelectronic and Computer Systems 2023 - 0 citations - Show abstract - Cite Abstract: The subject matter of this research revolves around addressing the escalating global health threat posed by cardiovascular diseases, which have become a leading cause of mortality in recent times. The goal of this study was to develop a comprehensive diet recommendation system tailored explicitly for cardiac patients. The primary task of this study is to assist both medical practitioners and patients in developing effective dietary strategies to counter heart-related ailments. To achieve this goal, this study leverages the capabilities of machine learning (ML) to extract valuable insights from extensive datasets. This approach involves creating a sophisticated diet recommendation framework using diverse ML techniques. These techniques are meticulously applied to analyze data and identify optimal dietary choices for individuals with cardiac concerns. In pursuit of actionable dietary recommendations, classification algorithms are employed instead of clustering. These algorithms categorize foods as "heart-healthy" or "not heart-healthy," aligned with cardiac patients’ specific needs. In addition, this study delves into the intricate dynamics between different food items, exploring interactions such as the effects of combining protein- and carbohydrate-rich diets. This exploration serves as a focal point for in-depth data mining, offering nuanced perspectives on dietary patterns and their impact on heart health. The method used central to the diet recommendation system is the implementation of the Neural Random Forest algorithm, which serves as the cornerstone for generating tailored dietary suggestions. To ensure the system’s robustness and accuracy, a comparative assessment involving other prominent ML algorithms—namely Random Forest, Naïve Bayes, Support Vector Machine, and Decision Tree, was conducted. The results of this analysis underscore the superiority of the proposed -based system, demonstrating higher overall accuracy in delivering precise dietary recommendations compared with its counterparts. In conclusion, this study introduces an advanced diet recommendation system using ML, with the potential to notably reduce cardiac disease risk. By providing evidence-based dietary guidance, the system benefits both healthcare professionals and patients, showcasing the transformative capacity of ML in healthcare. This study underscores the significance of meticulous data analysis in refining dietary decisions for individuals with cardiac conditions. 87.3% topic match

Provides AI-driven dietary recommendations for cardiac care. Utilizes machine learning for tailored diet plans, focusing on heart health. Employs classification algorithms to identify "heart-healthy" foods.

83.1%

0.0

2023

[10] Smart E-Healthcare Application For Predicting Choronic Diseases De Silva and Ms.Gaya Thamali Dassanayake Journal Not Provided 2023 - 0 citations - Show abstract - Cite 83.1% topic match

Proposes a smart e-healthcare application using machine learning. Detects chronic diseases early through patient data analysis for prevention and treatment. Doesn't specifically focus on personalized nutrition advice for chronic disease management.

70.5%

0.0

2021

[11] Διατροφικοί βιοδείκτες και μηχανική μάθηση για εφαρμογές εξατομικευμένης διατροφής και βελτιστοποίησης της ανθρώπινης υγείας Δημήτριος Παναγούλιας https://doi.org/10.26267/UNIPI_DIONE/854 2021 - 0 citations - Show abstract - Cite 70.5% topic match

Develops neural networks for personalized nutrition evaluation. Focuses on metabolomics to predict BMI and dietary patterns. Aligns with personalized nutrition but lacks chronic disease management focus.

67.7%

0.0

2023

[12] A Swarm Artificial Intelligence Approach for Effective Treatment of Chronic Conditions K. Kioskli and Spyridon Papastergiou 2023 19th International Conference on the Design of Reliable Communication Networks (DRCN) 2023 - 0 citations - Show abstract - Cite 67.7% topic match

Proposes a SwarmAI framework for personalized healthcare. Utilizes machine learning, including deep learning, for predictive modeling in chronic disease management. Aims for personalized intervention but lacks specific focus on nutritional advice.

60.2%

3.1

2017

[13] Live Personalized Nutrition Recommendation Engine Nitish Nag, ..., and Ramesh C. Jain Proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care 2017 - 22 citations - Show abstract - Cite 60.2% topic match

Develops a machine learning-based nutrition recommendation engine. Utilizes multimedia data for personalized, context-aware dietary advice tailored to eating out. Focuses on healthy meal dish selection rather than chronic disease management directly.

59.3%

0.0

2023

[14] A Review Of The Methods And Approaches For Integrating Genetics And Metabolomics Into Personalized Nutrition No author found Journal of Pharmaceutical Negative Results 2023 - 0 citations - Show abstract - Cite 59.3% topic match

Provides a review of personalized nutrition through genetics and metabolomics. Discusses machine learning for data integration in personalized dietary interventions. Highlights ethical considerations and future research directions in personalized nutrition.

58.1%

0.0

2024

[15] EARLY PREDICTION OF LIFESTYLE DISEASES Ijsrem Journal INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 2024 - 0 citations - Show abstract - Cite 58.1% topic match

Proposes a predictive framework using machine learning for disease risk. Focuses on early prediction of lifestyle-related diseases via lifestyle data analysis. While addressing disease prevention, it does not specifically cover personalized nutrition.

55.8%

2.7

2022

[16] Precision Medicine for Hypertension Patients with Type 2 Diabetes via Reinforcement Learning S. Oh, ..., and Jongyoul Park Journal of Personalized Medicine 2022 - 8 citations - Show abstract - Cite 55.8% topic match

Implements machine learning for personalized hypertension management. Uses reinforcement learning on electronic health records to tailor antihypertensive treatment. Focuses on hypertension in Type 2 Diabetes patients, lacking a direct nutrition focus.

53.3%

0.4

2022

[17] Personalized Nutrition for the Prevention and Treatment of Metabolic Diseases: Opportunities and Perspectives I. N. Napolsky and P. Popova Russian Journal for Personalized Medicine 2022 - 1 citations - Show abstract - Cite 53.3% topic match

Reviews personalized nutrition for metabolic disease management. Discusses use of omics technologies and data for dietary personalization. Mentions applications of computer programs in creating mobile apps for nutrition advice.

47.5%

0.8

2022

[18] A decision tree–based classifier to provide nutritional plans recommendations Omar Aguilar-Loja, ..., and Paola A. González 2022 17th Iberian Conference on Information Systems and Technologies (CISTI) 2022 - 2 citations - Show abstract - Cite 47.5% topic match

Provides a nutritional plan recommendation model via decision trees. Uses patient data alongside BMI and BMR for tailored nutritional advice. Focuses on general nutritional habits and early disease diagnosis potential.

45.6%

0.0

2022

[19] Machine Learning in Nutrition Research. Daniel Kirk, ..., and G. Camps Advances in nutrition 2022 - 0 citations - Show abstract - Cite 45.6% topic match

Introduces machine learning's role in nutrition research. Details ML applications in obesity, metabolic health, malnutrition, and precision nutrition. Offers framework for integrating ML in nutrition studies, lacking focus on chronic disease management.

41.7%

0.8

2022

[20] A Model for Analysis of Diseases Based on Nutrition Deficiency Using Random Forest Chaitanya Kosaraju, ..., and Gayathri Gali 2022 7th International Conference on Communication and Electronics Systems (ICCES) 2022 - 2 citations - Show abstract - Cite 41.7% topic match

Demonstrates machine learning's role in analyzing diet-disease relationships. Uses Random Forest, DT, and KNN to link diet to non-communicable diseases prevention. Outperforms Gaussian Nave Bayes in predicting diseases from nutritional intake, achieving 0.96 accuracy.

41.2%

0.0

2023

[21] Detection of Blood Glucose Level: A Machine Learning Approach S. Karmakar, ..., and S. Kundu 2023 7th International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech) 2023 - 0 citations - Show abstract - Cite 41.2% topic match

Employs machine learning to detect blood glucose levels. Uses decision trees, SVMs, and neural networks for early detection in diabetes management. Focuses on metabolic disorder prevention, not directly on personalized nutrition advice.

35.7%

0.0

2023

[22] “DiagnoMe” Mobile Application for Identifying and Predicting the Chronic Diseases J. Perera, ..., and Shanika Nethusara 2023 5th International Conference on Advancements in Computing (ICAC) 2023 - 0 citations - Show abstract - Cite 35.7% topic match

Introduces a mobile app for chronic disease prediction using ML. Focuses on RNN and Neural Network models for disease identification and prediction. Lacks specificity on personalized nutrition advice for chronic disease management.

35.5%

7.3

2021

[23] Perspective: Big Data and Machine Learning Could Help Advance Nutritional Epidemiology. J. Morgenstern, ..., and L. Anderson Advances in nutrition 2021 - 28 citations - Show abstract - Cite Abstract: The field of nutritional epidemiology faces challenges posed by measurement error, diet as a complex exposure, and residual confounding. The objective of this perspective article is to highlight how developments in big data and machine learning can help address these challenges. New methods of collecting 24-h dietary recalls and recording diet could enable larger samples and more repeated measures to increase statistical power and measurement precision. In addition, use of machine learning to automatically classify pictures of food could become a useful complimentary method to help improve precision and validity of dietary measurements. Diet is complex due to thousands of different foods that are consumed in varying proportions, fluctuating quantities over time, and differing combinations. Current dietary pattern methods may not integrate sufficient dietary variation, and most traditional modeling approaches have limited incorporation of interactions and nonlinearity. Machine learning could help better model diet as a complex exposure with nonadditive and nonlinear associations. Last, novel big data sources could help avoid unmeasured confounding by offering more covariates, including both omics and features derived from unstructured data with machine learning methods. These opportunities notwithstanding, application of big data and machine learning must be approached cautiously to ensure quality of dietary measurements, avoid overfitting, and confirm accurate interpretations. Greater use of machine learning and big data would also require substantial investments in training, collaborations, and computing infrastructure. Overall, we propose that judicious application of big data and machine learning in nutrition science could offer new means of dietary measurement, more tools to model the complexity of diet and its relations with diseases, and additional potential ways of addressing confounding. 35.5% topic match

Highlights potential of big data and machine learning in nutritional epidemiology. Discusses improving dietary measurement precision and modeling diet complexity using machine learning. Lacks direct focus on personalized nutrition for chronic disease management via machine learning applications.

32.0%

0.5

2019

[24] Application of Unsupervised Learning in Weight-Loss Categorisation for Weight Management Programs Oladapo Babajide, ..., and Thorkild IA Sorensen 2019 10th International Conference on Dependable Systems, Services and Technologies (DESSERT) 2019 - 3 citations - Show abstract - Cite 32.0% topic match

Demonstrates the use of unsupervised learning in weight management. Identifies weight loss categories in dietary intervention programs for optimized outcomes. Lacks direct application to chronic disease management or personalized nutrition advice.

29.4%

0.6

2023

[25] User Input Based Health Risk Assessment to Predict Diabetes, Obesity and Heart Risk factors Preethi Salian K, ..., and V. S 2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) 2023 - 1 citations - Show abstract - Cite 29.4% topic match

27.1%

1.0

2022

[26] An Extreme Value Analysis-Based Systemic Approach in Healthcare Information Systems: The Case of Dietary Intake Dimitrios P. Panagoulias, ..., and G. Tsihrintzis Electronics 2022 - 2 citations - Show abstract - Cite 27.1% topic match

25.9%

0.0

2023

[27] An Efficient Machine Learning Classification Model for Diabetes Prediction T. S. Sai Krishna, ..., and D. Haritha 2023 IEEE 12th International Conference on Communication Systems and Network Technologies (CSNT) 2023 - 0 citations - Show abstract - Cite 25.9% topic match

Develops ML models for diabetes prediction. Utilizes three ML algorithms for improved healthcare predictive analytics. Focuses on diabetes therapy potential, not personalized nutrition advice.

25.5%

0.6

2020

[28] Predictive Modelling for Chronic Disease: Machine Learning Approach Md. Rakibul Hoque and Mohammed Sajedur Rahman Proceedings of the 2020 4th International Conference on Compute and Data Analysis 2020 - 3 citations - Show abstract - Cite 25.5% topic match

23.2%

0.0

2023

[29] A Rule-Based Expert Advisory System for Restaurants Using Machine Learning and Knowledge-Based Systems Techniques Khalid M. O. Nahar, ..., and Brij B. Gupta Int. J. Semantic Web Inf. Syst. 2023 - 0 citations - Show abstract - Cite 23.2% topic match

21.8%

0.0

2022

[30] Diet Recommendation System based on Different Machine Learners Megh Shah, ..., and Dhairya Vyas International Journal of Scientific Research in Computer Science, Engineering and Information Technology 2022 - 0 citations - Show abstract - Cite 21.8% topic match

21.5%

0.5

2021

[31] Advancement in Precision Medicine and Recommendation System for Clinical Trials Using Deep Learning Methods A.P.Ponselvakumar, ..., and S.Swetha Jayashree IOP Conference Series: Materials Science and Engineering 2021 - 2 citations - Show abstract - Cite 21.5% topic match

19.7%

2.1

2022

[32] Development of Machine Learning Prediction Models to Explore Nutrients Predictive of Cardiovascular Disease Using Canadian Linked Population-Based Data. J. Morgenstern, ..., and Laura N. Anderson Applied physiology, nutrition, and metabolism = Physiologie appliquee, nutrition et metabolisme 2022 - 6 citations - Show abstract - Cite 19.7% topic match

19.1%

0.0

2024

[33] Machine learning's role in personalized medicine & treatment optimization Deepak Kumar, ..., and Shoumya Singh World Journal of Advanced Research and Reviews 2024 - 0 citations - Show abstract - Cite 19.1% topic match

Explores machine learning in personalized medicine and treatment optimization. Discusses analysis of genetic, lifestyle, and medical history data for tailored treatments but not nutritional advice. Highlights obstacles and ethical concerns in data sharing and use in healthcare; lacks specific focus on chronic disease nutrition management.

17.9%

0.5

2022

[34] Multi-Disease Prediction System using Machine Learning Vaibhav Raheja, ..., and Manisha Tiwari 2022 International Conference on Futuristic Technologies (INCOFT) 2022 - 1 citations - Show abstract - Cite 17.9% topic match

17.4%

0.0

2023

[35] Analyzing and Researching the Intermediate Layer of Alliance Medical Data Combined with Edge Computing Tse-Chuan Hsu, ..., and Shyh-Wei Chen 2023 IEEE International Conference on Software Services Engineering (SSE) 2023 - 0 citations - Show abstract - Cite 17.4% topic match

17.0%

0.9

2022

[36] Diet Recommendation using Predictive Learning Approaches Anjali Jain and Alka Singhal 2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) 2022 - 2 citations - Show abstract - Cite 17.0% topic match

16.9%

0.0

2023

[37] Machine Learning-Based Approach for Early Detection and Prediction of Chronic Diseases Shubhangi S. Shambharkar, ..., and Shubhangi M. Boarkar 2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0 (IDICAIEI) 2023 - 0 citations - Show abstract - Cite 16.9% topic match

16.3%

0.0

2023

[38] Prediction of Heart Disease from the Multiple Features Available in the Database by Applying Classification Models of Machine Learning Akash Rajak and R. Panda 2023 3rd International conference on Artificial Intelligence and Signal Processing (AISP) 2023 - 0 citations - Show abstract - Cite 16.3% topic match

Utilizes machine learning to predict heart disease. Classification models analyze patient health information for treatment decisions. Lacks explicit focus on personalized nutrition for chronic disease management.

15.8%

0.2

2020

[39] Enhanced Predictive Learning Approaches for Tweaked Diet Proposal System in Health Care Damodharan, ..., and V. Nallarasan International Journal of Emerging Trends in Engineering Research 2020 - 1 citations - Show abstract - Cite 15.8% topic match

15.8%

0.0

2023

[40] A Food Recommender System in Academic Environments Based on Machine Learning Models Abolfazl Ajami and B. Teimourpour ArXiv 2023 - 0 citations - Show abstract - Cite - PDF 15.8% topic match

14.5%

0.0

2022

[41] Smart Diagnosis of Disease Using Machine Learning B. Rajapandian, ..., and K. Abirami 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS) 2022 - 0 citations - Show abstract - Cite 14.5% topic match

14.3%

1.8

2023

[42] Machine Learning Model for Awareness of Diet Recommendation Ajit Kumar Rout, ..., and Nandam Sri Mouli 2023 International Conference on Inventive Computation Technologies (ICICT) 2023 - 3 citations - Show abstract - Cite 14.3% topic match

14.1%

1.9

2022

[43] Prospects and Pitfalls of Machine Learning in Nutritional Epidemiology S. Russo and S. Bonassi Nutrients 2022 - 5 citations - Show abstract - Cite 14.1% topic match

12.9%

10.2

2022

[44] Machine Learning in Nutrition Research Daniel Kirk, ..., and G. Camps Advances in Nutrition 2022 - 23 citations - Show abstract - Cite 12.9% topic match

11.3%

0.0

2023

[45] Chronic Disease prediction using Machine Learning Techniques: A Survey Nidhi Kumari and Madhu Gautam 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) 2023 - 0 citations - Show abstract - Cite Abstract: In current era people are very much prone to the ChronicDisease because of the ambient circumstances and lifestyle practices. Thus it became very vital task for doctors to predictit at its early stage so that the rate of mortality can be reduced. But, sometimes it became difficult and confusingtask for doctors to find out it at its very starting stage. With the help of machine learning technology it is possible todetect it at its starting state. Chronic disease is those types of illness which required lengthy medical attention. In order to diagnose chronic diseases, this study analyzes machine learning predictive model applications. Most of the expenditures associated with global health are attributable to the chronic diseases. Individuals who have these conditions require ongoing care. These days, the diagnosis and prognosis of many disorders routinely make use of predictive algorithms. In this work, we start with a primer on the most popular systems for categorizing chronic illness. Machine learning Algorithms have proven to be quite effective in producing results with a high level of correctness, thereby preventing the onset of fatal diseasein many patients and reducing the impact in those who are already affected. It has aided medical researchers and doctors worldwide in recognizing patterns in patients, resulting inchronic sickness detection at an early stage. Finally, some survey was chosen to showcase all modeling procedure in the uniform manner which describes its treatment plan and functional results of specific maladies, alongside with their related advantages and drawbacks. The most important studies that have used machine learning classification approaches in this field of study are reviewed. There is a sizable table that contrasts and compares the articles. In the near future, these models are anticipated to become more common in medical practice and extremely useful in the grouping and diagnosis off at alma ladies. 11.3% topic match

11.2%

0.0

2023

[46] Interpretable Medical Recommendations Based on SHAPs Yuanyuan Wu, ..., and Yu Zhang 2023 IEEE 6th International Conference on Pattern Recognition and Artificial Intelligence (PRAI) 2023 - 0 citations - Show abstract - Cite 11.2% topic match

10.4%

3.3

2021

[47] A Recommender System for Healthy Food Choices: Building a Hybrid Model for Recipe Recommendations using Big Data Sets Pallavi Chavan, ..., and J. Isaacs Hawaii International Conference on System Sciences 2021 - 13 citations - Show abstract - Cite 10.4% topic match

10.3%

1.1

2022

[48] Diet Recommendation System based on Different Machine Learners: A Review Megh Shah, ..., and Dhairya Vyas 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS) 2022 - 3 citations - Show abstract - Cite 10.3% topic match

10.0%

0.0

2022

[49] Prediction of Nutritional Requirements for Children’s Growth and Adolescents using Machine Learning A. Hermanto, ..., and Paul L Tahalele 2022 International Seminar on Application for Technology of Information and Communication (iSemantic) 2022 - 0 citations - Show abstract - Cite 10.0% topic match

9.4%

1.3

2023

[50] Gastrointestinal failure, big data and intensive care P. Singer, ..., and Orit Raphaeli Current Opinion in Clinical Nutrition and Metabolic Care 2023 - 2 citations - Show abstract - Cite 9.4% topic match

8.7%

0.0

2023

[51] Predictive Modeling for Disease Progression in Chronic Conditions Using Machine Learning Dr. T. S. Umamaheswari, ..., and Dr. Suman Kumar Swarnkar 2023 6th International Conference on Contemporary Computing and Informatics (IC3I) 2023 - 0 citations - Show abstract - Cite 8.7% topic match

8.3%

5.0

2019

[52] Deep learning opens new horizons in personalized medicine G. Papadakis, ..., and K. Marias Biomedical Reports 2019 - 29 citations - Show abstract - Cite 8.3% topic match

8.0%

2.5

2021

[53] Mobile 5P-Medicine Approach for Cardiovascular Patients I. Pires, ..., and N. Garcia Sensors (Basel, Switzerland) 2021 - 8 citations - Show abstract - Cite 8.0% topic match

7.5%

0.0

2021

[54] Predictive Analysis and Detection of Heart Disease Using Machine Learning Approach K. Raghuveer Journal Not Provided 2021 - 0 citations - Show abstract - Cite 7.5% topic match

7.3%

0.0

2023

[55] Performance and Analysis of Predicting Chronic Disease Using Machine Learning Techniques K. Hima Bindu, ..., and M. Satyanarayana Reddy International Journal of Scientific Research in Science, Engineering and Technology 2023 - 0 citations - Show abstract - Cite 7.3% topic match

7.1%

0.7

2019

[56] Predictive AI Models for the Personalized Medicine L. Lella, ..., and R. Pastorino International Conference on Health Informatics 2019 - 4 citations - Show abstract - Cite 7.1% topic match

6.7%

0.5

2021

[57] Human Body Analysis and Diet Recommendation System using Machine Learning Techniques M. Geetha, ..., and V. Muthulakshmi Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICASISET 2020, 16-17 May 2020, Chennai, India 2021 - 2 citations - Show abstract - Cite 6.7% topic match

6.1%

0.2

2019

[58] Machine Learning for Personalized Medicine: Clinical Outcome Prediction and Diagnosis : Plenary Talk Ž. Ćojbašić 2019 IEEE 13th International Symposium on Applied Computational Intelligence and Informatics (SACI) 2019 - 1 citations - Show abstract - Cite Abstract: Machine learning and computational intelligence have been applied to a wide range of medical problems to assist medical professionals in decision-making. Especially artificial neural networks, fuzzy systems and powerful hybrid neuro-fuzzy approaches have already proven their strong potentials in medicine. This is especially important and interesting in emerging field of personalized medicine, which is often described as providing ”the right patient with the right drug at the right dose at the right time” and represents tailoring of medical treatment to the individual patient characteristics, needs and preferences. Machine learning focuses on the development of computer programs that can access data and use it to learn by themselves. Regarding medical applications concerning prediction of patient's clinical outcome, machine learning can be considered as a data-driven analytic approach that specializes in the integration of multiple risk factors into a predictive tool. In this talk hypothesis has been considered that machine learning based models may help to improve prediction of clinical outcome in various medical fields, in comparison to traditional statistical and scoring approaches. This hypothesis has been tested with our own results from several studies. Our experiences have been considered regarding artificial neural networks based prediction of cerebral palsy in infants with central coordination disturbance, adaptive neuro-fuzzy estimation of autonomic nervous system parameters effect on heart rate variability, machine learning leukemia clinical outcome prediction, neural prediction of mortality in spontaneous intracerebral hemorrhage based on initial clinical parameters and others. Finally, to demonstrated potentials of machine learning in medical diagnosis, our results concerning cervical cancer detection by improving standard screening tests have been considered. 6.1% topic match

6.0%

36.1

2019

[59] Metabolomics toward personalized medicine. M. Jacob, ..., and A. A. Abdel Rahman Mass spectrometry reviews 2019 - 204 citations - Show abstract - Cite 6.0% topic match

5.5%

0.0

2020

[60] Artificial Intelligence for Precision Medicine and Better Healthcare K. Jain and Vinit Shah Journal Not Provided 2020 - 0 citations - Show abstract - Cite 5.5% topic match

4.9%

0.0

2023

[61] Recent Challenges in Medical Science using Machine Learning Techniques: A Review Milind Shah, ..., and Krupa Raja 2023 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) 2023 - 0 citations - Show abstract - Cite 4.9% topic match

4.9%

0.5

2020

[62] Predictive Biomarker Identification for Biopharmaceutical Development Xin Huang, ..., and Ivan S. F. Chan Statistics in Biopharmaceutical Research 2020 - 2 citations - Show abstract - Cite 4.9% topic match

4.3%

0.0

2023

[63] Multi Disease Prediction Using HDO Machine Learning Approach R. Gulhane and S. Gupta International Journal on Recent and Innovation Trends in Computing and Communication 2023 - 0 citations - Show abstract - Cite 4.3% topic match

4.1%

11.7

2021

[64] Preventing dataset shift from breaking machine-learning biomarkers J'eroome Dockes, ..., and J B Poline GigaScience 2021 - 40 citations - Show abstract - Cite - PDF 4.1% topic match

3.8%

10.2

2019

[65] Computer-aided biomarker discovery for precision medicine: data resources, models and applications Yuxin Lin, ..., and Bairong Shen Briefings in bioinformatics 2019 - 57 citations - Show abstract - Cite 3.8% topic match

3.7%

0.8

2020

[66] Integrated Prediction System for Chronic Disease Diagnosis to Ensure Better Healthcare G. K and K. K. Digital Health eJournal 2020 - 4 citations - Show abstract - Cite 3.7% topic match

3.3%

1.6

2023

[67] Application of Machine Learning in Disease Prediction A. Garg and Deepika Bansal International Journal for Research in Applied Science and Engineering Technology 2023 - 3 citations - Show abstract - Cite 3.3% topic match

3.2%

4.9

2023

[68] Comparison and Analysis of Various Machine Learning Algorithms for Disease Prediction Sriya Kanamarlapudi, ..., and Srithar S 2023 7th International Conference on Computing Methodologies and Communication (ICCMC) 2023 - 9 citations - Show abstract - Cite 3.2% topic match

2.7%

0.0

2017

[69] An example of how false conclusions could be made with personalized health tracking and suggestions for avoiding similar situations O. Demasi and B. Recht ArXiv 2017 - 0 citations - Show abstract - Cite - PDF 2.7% topic match

2.7%

0.0

2021

[70] Machine Learning Techniques for Extracting Relevant Features from Clinical Data for COVID-19 Mortality Prediction Michele Fraccaroli, ..., and Alice Bizzarri 2021 IEEE Symposium on Computers and Communications (ISCC) 2021 - 0 citations - Show abstract - Cite 2.7% topic match

2.6%

2.6

2020

[71] BIG DATA ANALYTICS IN HEART DISEASES PREDICTION A. Ismail, ..., and I. El-Henawy Journal Not Provided 2020 - 13 citations - Show abstract - Cite 2.6% topic match

2.5%

0.0

2019

[72] Morality Prediction Model in Cardiovascular Disease with Significant Feature Selection and Hybrid KNN Classification Technique C.Sowmiya and P. Sumitra International Journal of Innovative Technology and Exploring Engineering 2019 - 0 citations - Show abstract - Cite 2.5% topic match

2.2%

0.6

2020

[73] Healthcare analytics by engaging machine learning Pragathi Penikalapati and A Nagaraja Rao https://doi.org/10.31763/SITECH.V1I1.32 2020 - 3 citations - Show abstract - Cite 2.2% topic match

2.0%

0.2

2015

[74] Development of health diagnostics based on personalized medical models D. Hudson, ..., and S. Hudson 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2015 - 2 citations - Show abstract - Cite 2.0% topic match

1.8%

0.0

2022

[75] Performance Analysis for Chronic Disease Prediction using Various Data Mining Techniques S. Rajeashwari and Ramasamy Naidu 2022 3rd International Conference on Electronics and Sustainable Communication Systems (ICESC) 2022 - 0 citations - Show abstract - Cite 1.8% topic match

1.7%

51.5

2020

[76] Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine Zeeshan Ahmed, ..., and Xinqi Dong Database: The Journal of Biological Databases and Curation 2020 - 257 citations - Show abstract - Cite Abstract: Abstract Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better and economically personalized treatments. Identifying the best pathway to personalized and population medicine involves the ability to analyze comprehensive patient information together with broader aspects to monitor and distinguish between sick and relatively healthy people, which will lead to a better understanding of biological indicators that can signal shifts in health. While the complexities of disease at the individual level have made it difficult to utilize healthcare information in clinical decision-making, some of the existing constraints have been greatly minimized by technological advancements. To implement effective precision medicine with enhanced ability to positively impact patient outcomes and provide real-time decision support, it is important to harness the power of electronic health records by integrating disparate data sources and discovering patient-specific patterns of disease progression. Useful analytic tools, technologies, databases, and approaches are required to augment networking and interoperability of clinical, laboratory and public health systems, as well as addressing ethical and social issues related to the privacy and protection of healthcare data with effective balance. Developing multifunctional machine learning platforms for clinical data extraction, aggregation, management and analysis can support clinicians by efficiently stratifying subjects to understand specific scenarios and optimize decision-making. Implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized and population medicine at lower costs. In this study, we focused on analyzing and discussing various published artificial intelligence and machine learning solutions, approaches and perspectives, aiming to advance academic solutions in paving the way for a new data-centric era of discovery in healthcare. 1.7% topic match

1.6%

None

[77] MODELLING & UNDERSTANDING THE HUMAN BODY WITH SWARMSCRIPT S. Mammen, ..., and C. Jacob Journal Not Provided None - 3 citations - Show abstract - Cite 1.6% topic match

1.5%

0.2

2020

[78] DISEASE PREDICTION USING MACHINE LEARNING OVER BIGDATA Afroz Fatima Journal of emerging technologies and innovative research 2020 - 1 citations - Show abstract - Cite 1.5% topic match

1.4%

1.0

2023

[79] Multiple Disease Prediction System using Machine Learning and Streamlit Laxmi Deepthi Gopisetti, ..., and Hari Priya Kodali 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT) 2023 - 2 citations - Show abstract - Cite 1.4% topic match

1.3%

0.0

2022

[80] Predicting the development of hypertension using machine learning models in the remote cardiomonitoring subsystem E. Belozerova, ..., and Yu. I. Usov Manager Zdravoochranenia 2022 - 0 citations - Show abstract - Cite 1.3% topic match

1.1%

0.5

2021

[81] A S URVEY ON E FFICIENT USE OF D ATA M INING AND M ACHINE L EARNING IN H EALTHCARE A PPLICATIONS Aishwarya Hastak, ..., and Bhushan Inje Journal Not Provided 2021 - 2 citations - Show abstract - Cite 1.1% topic match

1.1%

2.8

2021

[82] Advancements within modern machine learning methodology: Impacts and prospects for biomarker discovery. Dakila A Ledesma, ..., and S. Richards Current medicinal chemistry 2021 - 11 citations - Show abstract - Cite Abstract: Background The adoption of biomarkers as part of high-throughput, complex microarray or sequencing data has necessitated the discovery and validation of these data through machine learning. Machine learning has remained a fundamental and indispensable tool due to its efficacy and efficiency in both feature extraction of relevant biomarkers as well as the classification of samples as validation of the discovered biomarkers. Objectives This review aims to present the impact and ability of various machine learning methodologies and models to process high-throughput, high-dimensionality data found within mass spectrometry, microarray, and DNA/RNA-sequence data; data that precluded biomarker discovery prior to the use of machine learning. Methods A vast array of literature highlighting machine learning for biomarker discovery was reviewed, resulting in the eligibility of 21 machine learning algorithms/networks and 3 combinatory architectures, spanning 17 fields of study. This literature was screened to investigate the usage and development of machine learning within the framework of biomarker discovery. Results Out of the 93 papers collected, a total of 62 biomarker studies were further reviewed across different subfields-49 of which employed machine learning algorithms, and 13 of which employed neural network-based models. Through application, innovation, and creation of tools in biomarker-related machine learning methodologies, its use allowed for the discovery, accumulation, validation, and interpretation of biomarkers within varied data formats, sources, as well as fields of study. Conclusion The use of machine learning methodologies for biomarker discovery is critical to the analysis of various types of data used for biomarker discovery, such as mass spectrometry, nucleotide and protein sequencing, and image (e.g. CT-scan) data. Further studies containing more standardized techniques for evaluation, and the use of cutting-edge machine learning architectures may lead to more accurate and specific results. 1.1% topic match

1.0%

10.1

2023

[83] Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review Qasem Al-Tashi, ..., and Jia Wu International Journal of Molecular Sciences 2023 - 17 citations - Show abstract - Cite 1.0% topic match

0.8%

0.0

2024

[84] A Predictive Study of Machine Learning and Deep Learning Procedures Over Chronic Disease Datasets Nimay Seth Feb-Mar 2024 2024 - 0 citations - Show abstract - Cite Abstract: People's health and well-being are not given priority in the technological and Internet-savvy world we live in. People are becoming worse because they don't regularly attend the hospital for checkups due to job and unanticipated events. Most people nowadays suffer from one or more chronic illnesses, such as diabetes, hypothyroidism, heart disease, breast cancer, and dermatology. According to the World Health Organization (WHO), these chronic illnesses account for half of all fatalities in most nations and are the main cause of premature mortality. Patients who are identified early on potentially have their condition stop progressing. Many dispersed studies clearly demonstrated that conventional approaches to diagnosing chronic illnesses are prone to prejudice and heterogeneity among physicians, making it difficult to promptly and precisely diagnose problems. Still, Despite the availability of up-to-date information and a variety of machine learning-based methods, there have been enormous published efforts demonstrating that machine learning (ML)/deep learning (DL) based approach can considerably enhance the timely estimation of various health conditions. However, precise diagnosis of such diseases remains a difficulty. There are many machine learning-based techniques and current knowledge available, however despite this, a great deal of published research has shown that machine learning/deep learning based approach can considerably enhance the timely estimation of various health conditions. However, precise diagnosis of such diseases remains a difficulty. In order to tackle this problem, this work uses the UCI/KAGGLE ML/DL disease dataset to evaluate various ML/DL procedures and explores how different machine learning algorithms forecast chronic diseases. Accuracy and confusion matrix are used to verify the results. In order to help inexperienced researchers comprehend the disease prediction function of ML/DL-based techniques and determine the direction of Upcoming research, this study also discusses the advantages and disadvantages of accessible disease prediction schemes. 0.8% topic match

0.8%

0.0

2020

[85] Prediction Of Heart Disease And Diabetes Using Machine Learning Akhila Maram, ..., and N. Niriksha Journal Not Provided 2020 - 0 citations - Show abstract - Cite 0.8% topic match

0.7%

0.0

2023

[86] PredictiCare: Empowering Health Predictions with Machine Learning D. Manikkannan INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 2023 - 0 citations - Show abstract - Cite 0.7% topic match

0.7%

0.9

2021

[87] Use of Machine Learning Techniques to Predict Diabetes at an Early Stage Sarra Samet, ..., and Issam Bendib 2021 International Conference on Networking and Advanced Systems (ICNAS) 2021 - 3 citations - Show abstract - Cite 0.7% topic match

0.6%

0.8

2022

[88] Machine learning can improve the development of evidence-based dietary guidelines L. Bodnar, ..., and A. Naimi Public Health Nutrition 2022 - 2 citations - Show abstract - Cite Abstract: The Dietary Guidelines for Americans provide dietary advice for health promotion and disease prevention and serve as the cornerstone to all other US Federal nutrition policy and education efforts(1). Defining what populations should eat to optimise health is challenging, however, owing to the complexity of the diet. A recent report by the National Academies of Science, Engineering, and Medicine(2) recommended approaches to improve the process of establishing the Dietary Guidelines to account for this complexity, including a call for more advanced analytic techniques for analysing existing dietary data. In this commentary, we highlight several analytic challenges in nutrition and how they can be addressed using a machine learning, a set of flexible algorithms and methods to model complex relations in data. It is well recognised that foods are eaten in complex combinations with potential antagonistic and synergistic interactions that may impact long-term health(3,4). Some research has captured this complexity by testingwhole-diet interventions, such as those rich in vegetables, fruits, whole grains, fish, nuts and beans in the DASH (Dietary Approaches to Stop Hypertension) trial(5–7). Whole-diet interventions have suggested that the most relevant exposure is the totality of the diet(3), typically conceptualised as a multidimensional and dynamic construct referred to as ‘dietary patterns’(8). Dietary patterns constitute the consumption of an array of foods and beverages in different amounts and combinations. The 2010 version of the Dietary Guidelines for Americans recognised the importance of dietary patterns(9), and its emphasis has remained in each subsequent edition(10,11). Unfortunately, high-quality randomised trials of wholediet interventions are much more difficult to conduct than dietary supplement interventions(12). Challenges include their sheer complexity, identifying suitable comparators, blinding participants and allocators, and numerous complications involving potential non-adherence to the intervention protocols(13). As a result, observational studies of dietary patterns and disease risk form a majority of the evidence base for dietary recommendations(1). Consequently, the validity of inferences drawn from observational data is critical for informing evidence-based dietary guidelines. Researchers in nutritional epidemiology tend to focus on improving validity primarily by addressing problems with dietary measurement error(14,15). Yet, despite urging from nutrition experts(8), less research has focused on reducing other potential biases and problems with interpretability that can arise when using conventional methods for analysing nutritional epidemiologic data. 0.6% topic match

0.5%

0.0

2023

[89] Artificial Intelligence Powered Early Detection of Heart Disease T. A. Mohanaprakash, ..., and Anbarasa Pandian A 2023 3rd International Conference on Smart Data Intelligence (ICSMDI) 2023 - 0 citations - Show abstract - Cite 0.5% topic match

0.4%

9.5

2022

[90] Identification and Prediction of Chronic Diseases Using Machine Learning Approach Rayan Alanazi Journal of Healthcare Engineering 2022 - 27 citations - Show abstract - Cite 0.4% topic match

0.3%

4.7

2021

[91] Applicability of machine learning techniques in food intake assessment: A systematic review Larissa Oliveira Chaves, ..., and J. Bressan Critical Reviews in Food Science and Nutrition 2021 - 16 citations - Show abstract - Cite 0.3% topic match

0.2%

0.0

2023

[92] A Comparative Analysis of Machine Learning Algorithms for Classification of Diabetes Utilizing Confusion Matrix Analysis Maad M. Mijwil and Mohammad Aljanabi Baghdad Science Journal 2023 - 0 citations - Show abstract - Cite 0.2% topic match

0.2%

0.0

2023

[93] Food Nutrient Extraction Based on Image Recognition and Entity Extraction Hanzhong Gao, ..., and Jianwei Gao 2023 19th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) 2023 - 0 citations - Show abstract - Cite 0.2% topic match

0.2%

4.2

2019

[94] Reinforcement learning in clinical medicine: a method to optimize dynamic treatment regime over time. Wentao Bao Annals of translational medicine 2019 - 23 citations - Show abstract - Cite 0.2% topic match

0.2%

0.0

2023

[95] Multi chronic disease prediction: A survey Chunduru Anilkumar, ..., and Surapureddi Mrudula Applied and Computational Engineering 2023 - 0 citations - Show abstract - Cite 0.2% topic match

0.1%

0.0

2023

[96] Optimization of The Machine Learning Approach using Optuna in Heart Disease Prediction Sri Hadianti Journal Medical Informatics Technology 2023 - 0 citations - Show abstract - Cite 0.1% topic match

0.1%

0.0

2022

[97] in Current MINING OF NUTRITIONAL INGREDIENTS IN FOOD FOR DISEASE ANALYSIS M. Sai and N. Suresh Journal Not Provided 2022 - 0 citations - Show abstract - Cite Abstract: In the prevention and treatment of noncommunicable illnesses, such as cancer, it has long been recognised that a well-balanced, nutritious diet is essential (NCDs). Research has been conducted on the nutritional components of food that are beneficial in the rehabilitation of noncommunicable diseases, on the other hand, but only a small amount has been done. Because of the use of data mining technologies, we were able to conduct a thorough investigation into the association between food components and illnesses. In order to get started, we compiled a list of more than 7,000 disorders, after which we decided which foods were recommended for each condition and which foods were strictly forbidden. Using the China Food Nutrition as a reference, we went on to predict which nutritional ingredients are most likely to have beneficial impacts on disease using noise-intensity and information entropy. At the conclusion of the research, we proposed an improved technique called CVNDA Red, which is based on rough sets and is used to select the necessary core ngredients from among the most favourable nutritional components. CVNDA Red is based on rough sets and is used to select the necessary core ngredients from among the most favourable nutritional components. A contraction of the phrases CVNDA and Red, which translates as "CVNDA Red." CVNDA Red is a trademark of the CVNDA Corporation. According to our knowledge, this is the first research in China to analyse the association between nutritious elements in food and illnesses via the use of data mining techniques based on rough set theory, which we believe is the case. We have shown via experiments carried out on real-world information that our data mining technique outperforms the conventional statistical approach, with accuracy 1.682 times greater than the conventional statistical methodology. By way of aside, our research has been beneficial in uncovering the first two to three nutritional components contained within foods that may be used to aid in the rehabilitation of a range of common conditions such as type 2 diabetes, hypertension, and cardiovascular disease. These experimental findings indicate the utility of using data mining to choose nutritional components in food for illness analysis when choosing nutritional ingredients in food when selecting nutritional elements in food when selecting nutritional components in food. 0.1% topic match

0.1%

2.3

2021

[98] Predicting heart disease using hybrid machine learning model G. Renugadevi, ..., and R. Gowthamani Journal of Physics: Conference Series 2021 - 9 citations - Show abstract - Cite 0.1% topic match

0.1%

0.0

2019

[99] The future of oncology: Using machine learning to drive the development of personalized medicine E. Brant University of Toronto Medical Journal 2019 - 0 citations - Show abstract - Cite 0.1% topic match

0.0%

0.0

2023

[100] Challenges for Predictive Modeling with Neural Network Techniques using Error-Prone Dietary Intake Data Dylan Spicker, ..., and Kevin W. Dodd ArXiv 2023 - 0 citations - Show abstract - Cite - PDF Abstract: Dietary intake data are routinely drawn upon to explore diet-health relationships. However, these data are often subject to measurement error, distorting the true relationships. Beyond measurement error, there are likely complex synergistic and sometimes antagonistic interactions between different dietary components, complicating the relationships between diet and health outcomes. Flexible models are required to capture the nuance that these complex interactions introduce. This complexity makes research on diet-health relationships an appealing candidate for the application of machine learning techniques, and in particular, neural networks. Neural networks are computational models that are able to capture highly complex, nonlinear relationships so long as sufficient data are available. While these models have been applied in many domains, the impacts of measurement error on the performance of predictive modeling has not been systematically investigated. However, dietary intake data are typically collected using self-report methods and are prone to large amounts of measurement error. In this work, we demonstrate the ways in which measurement error erodes the performance of neural networks, and illustrate the care that is required for leveraging these models in the presence of error. We demonstrate the role that sample size and replicate measurements play on model performance, indicate a motivation for the investigation of transformations to additivity, and illustrate the caution required to prevent model overfitting. While the past performance of neural networks across various domains make them an attractive candidate for examining diet-health relationships, our work demonstrates that substantial care and further methodological development are both required to observe increased predictive performance when applying these techniques, compared to more traditional statistical procedures. 0.0% topic match

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